Method for analyzing the movements of a person, and device for implementing same

ABSTRACT

A method for analyzing at least one sequence of movements performed by a person, including steps of segmenting the sequence of movements into time units, each associated with a time (t0 to tn), determining for each instant, positions of at least some characteristic points of the person using an acquisition system providing raw data, assigning at each instance at least one position code for the characteristic points as a function for each of them of the determined position to form a combination of position codes at each instant, assigning at least one elementary action code for each given instant corresponding to the combination of position codes at the given instant, and syntactically verifying the elementary action codes and/or position codes from at least one structured language to refine the raw data relating to the positions of the characteristic points.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application claims the benefit of the International Application No.PCT/EP2019/078244, filed on Oct. 17, 2019, and of the French patentapplication No. 1859634 filed on Oct. 18, 2018, the entire disclosuresof which are incorporated herein by way of reference.

FIELD OF THE INVENTION

The present application relates to a method for analyzing the movementsof a person and to a device for implementing the same. Depending on theapplication, the method of the invention may be used to evaluate thestrenuousness of work and/or to optimize certain movements at aworkstation.

BACKGROUND OF THE INVENTION

Hitherto, the strenuousness of work has been assessed in a subjectivemanner, in view of certain factors such as:

the manual handling of heavy loads,

strenuous postures, defined as forced positions of the articulations, or

repetitive work, characterized by the repetition of the same movement,at a forced rate, with a specified cycle time.

Enterprises attempting to reduce the strenuousness of work for theiremployees, for example in order to limit the occurrence ofmusculoskeletal disorders, have evaluation tools such as questionnairesand scorecards, but no tool for quantifying strenuousness.

Enterprises may engage ergonomists to improve the working conditions ofpersons at their workstations. The procedure followed by an ergonomistcomprises a first phase of observing the workstation, a second phase ofanalysis based on ergonomic scorecards, and a third step of drawing uprecommendations, which are usually a compromise between the ergonomist'sanalysis and the different viewpoints of the persons moving at theworkstation.

This procedure is not entirely satisfactory, because the firstobservation phase is relatively lengthy and incomplete. Moreover, sincethe recommendations drawn up by the ergonomist are based onobservations, rather than on precise biomechanical measurements, thequality of his recommendations is highly dependent on his level ofexpertise.

In the field of sport, augmented reality and animation, there aredevices for monitoring a person's movements, which can be used tomonitor the movements of different segments of a person moving in ascene. According to a first embodiment, such a device comprises aplurality of cameras arranged so as to cover the scene, and markerscarried by the person, which are arranged so as to identify thedifferent segments of the person.

Although the devices of this first embodiment may be used to monitor thedifferent movements of the different segments of a person veryprecisely, it may be difficult to use them in the context of workstationimprovement, for the following reasons:

Because of the high costs of manufacture and use, such devices cannot bedeployed in the context of the analysis and improvement of workstations.

Because of the fragility and price of some of their components, thesedevices are generally used in laboratory-like conditions, which areincompatible with an environment such as a building site in theconstruction industry.

The intrusive quality of the device, due to the placing of markers on aperson, may falsify the measurements.

The equipment preparation and calibration times are relatively long.

According to a second embodiment, originating notably from the videogames field, a device for monitoring the movements of a player comprisesa movement sensor in the form of a depth camera positioned facing theplayer, together with software for processing the signal from thecamera, configured for identifying the different segments of the playerand their movements, for the purpose of representing these differentsegments in the form of a wireframe avatar. The costs of manufacture anduse of devices of this second embodiment are markedly lower than thoseof the first embodiment, and are therefore compatible with a method ofworkstation improvement. Furthermore, they do not require the fitting ofmarkers on persons.

Despite these advantages, a movement monitoring device according to thesecond embodiment cannot be used as such to obtain a reliable dynamicmodel of a person for the purpose of improving his workstation orreducing strenuousness.

The present invention is intended to overcome all or some of thedrawbacks of the prior art.

SUMMARY OF THE INVENTION

To this end, the invention relates to a method for analyzing at leastone sequence of movements performed by a person having a set ofcharacteristic points, each movement comprising a succession ofelementary actions. The method comprises a step of observing thesequence of movements by means of at least one acquisition systemconfigured for supplying raw data relating to the positions of at leastsome characteristic points of the set of characteristic points.

According to the invention, the method comprises:

a step of segmenting the sequence of movements into time units, eachassociated with an instant,

a step of determining, for each instant, positions of at least somecharacteristic points of the set of characteristic points, using theacquisition system,

a step of assigning, at each instant, at least one position code for atleast some characteristic points of the set of characteristic points, onthe basis, for each of them, of the determined position, so as to obtaina combination of position codes at each instant,

a step of assigning at least one elementary action code for each giveninstant, corresponding to the combination of position codes at the giveninstant, and

a step of syntactically verifying the elementary action codes and/orposition codes on the basis of at least one structured language whichhas at least one rule and at least one lexicon, in order to refine theraw data relating to the positions of the characteristic points.

This solution enables reliable and precise monitoring of a person'smovements to be obtained on the basis of raw data.

BRIEF DESCRIPTION OF THE DRAWINGS

Other characteristics and advantages will be apparent from the followingdescription of the invention, this description being provided solely byway of example, with reference to the attached drawings, in which:

FIG. 1 is a schematic representation of a step of calibrating a devicefor monitoring a person's movements, which illustrates an embodiment ofthe invention,

FIG. 2 is a schematic representation of the device for monitoring themovements of a person visible in FIG. 1, after the calibration step,

FIG. 3 is a schematic representation of a step of self-learning.

FIG. 4 is a schematic representation of a step of the different realpositions of a real movement of a person's forearm.

FIG. 5 is a schematic representation of the different virtual positionsdetermined by a movement monitoring device on the basis of theobservation of the real movement visible in FIG. 4,

FIG. 6 is a translation of the real movement visible in FIG. 4, in theform of a succession of characters of a writing system,

FIG. 7 is a translation of the different positions visible in FIG. 5,using the same writing system as that used in FIG. 6,

FIG. 8 is a schematic representation of an acquisition unit,illustrating an embodiment of the invention,

FIG. 9 is a schematic representation of a device for monitoring aperson's movements, illustrating an embodiment of the invention,

FIG. 10 is a schematic representation of a step of the differentarticulations of a person,

FIG. 11 is a table showing the states of the different articulations andthe elementary movements at different instants, together with themovements performed by a person,

FIG. 12 is a table showing the distances between the articulations at agiven instant.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 9 shows, at 10, a device for analyzing a person's movements,comprising at least an acquisition unit 12 and at least a processing andanalysis system 14. According to one application, this device foranalyzing a person's movements 10 is configured to obtain a dynamicmodel of the person. Thus, the analysis of a person's movements is notlimited to the analysis of the kinematics of the movements. According tothe invention, the analysis of the movements covers the analysis of thekinematics of the movements and the analysis of the forces, powersand/or energies produced and associated with the movements.

According to a configuration illustrated in FIGS. 1 and 2, anacquisition unit 12 is supported by a tripod 16. Evidently, theinvention is not limited to this arrangement. Regardless of theconfiguration, each acquisition unit 12 is positioned and oriented so asto view a scene 18 in which at least one person 20 may move.

For the remainder of the description, a person 20 (visible in FIG. 2,for example) comprises a plurality of real segments, referenced in ageneral way by 22 or separately by 22.1, 22.2, 22.3 (as illustrated inFIG. 4), connected by real articulations, referenced in a general way byA or separately by A.0 to A.13 (as illustrated in FIGS. 4 and 10). Thesereal segments 22 correspond to the different parts of his body (trunk,upper right or left arm, right or left forearm, right or left hand,etc.), which are articulated to each other by the real articulations A,which may be links of the ball type (three possible rotations) or thepivot type (one possible rotation).

Each acquisition unit 12 comprises at least one acquisition sensor 26configured for determining a raw virtual position, in a virtualreference frame, for each of the virtual segments corresponding to atleast some of the real segments 22 and/or for each of the virtualarticulations corresponding to at least some real articulations A.

According to a non-limiting embodiment, each acquisition sensor 26comprises an RGB-D depth camera 28 configured for sending a signal onthe basis of the captured image stream, together with a controller 30 inwhich is implemented an image processing software configured forprocessing the signal from the camera 28 and for deducing therefrom theraw virtual position of each virtual segment and/or each virtualarticulation corresponding to the real segments 22 and/or to the realarticulations A filmed by the depth camera 28.

The device for analyzing a person's movements 10 may comprise otheracquisition sensors 26, such as force or power sensors.

According to one configuration, the acquisition sensor(s) 26 is/areconfigured for supplying at their output a set of raw data comprising atleast some anatomic and/or morphological data on the person (length,weight, center of gravity, etc., of each virtual segment) and/or atleast some dynamic data on the person (rotation vector of each pair ofreal virtual segments, forces, moments applied to each segment, speed ata given point of each segment, etc.).

According to one embodiment, shown in FIGS. 8 and 9 for example, thedevice for analyzing a person's movements comprises a plurality ofacquisition sensors 26. These acquisition sensors 26 may determineredundant values for some data in the set of raw data. In this case,these redundant values are merged so as to establish a unique value foreach raw data element.

The acquisition sensors 26 are not described further, since some of themmay be identical to the movement analysis devices of the secondembodiment of the prior art.

As in the prior art, the acquisition sensor 26 or the acquisitionsensors 26 supply, for each virtual segment and/or each virtualarticulation, a less reliable position, called a raw position, which isnot satisfactory in the context of a precise analysis of the movementsof the real segments and/or the real articulations of a person.

Regardless of the embodiment, the acquisition sensor or sensors 26is/are configured for determining, on the basis of the observation of aperson, a set of raw data that may be used for positioning in a virtualreference frame a set of virtual segments and/or virtual articulationscorresponding to the real segments and/or the real articulations of theobserved person. The acquisition sensor or sensors 26 is/are configuredfor determining, on the basis of the observation of a person, a dynamicmodel of the observed person.

For the remainder of the description, “a sensor or the sensor” is alsotaken to mean either a single acquisition sensor 26 or a plurality ofacquisition sensors 26 that may be used to obtain a set of raw data.

According to one embodiment, the device for analyzing a person'smovements 10 comprises a processing module 32 configured for correctingthe value of each raw data element measured by the acquisition sensor26, in order to establish a set of refined data.

The degree of correction of each raw data element is such that thevirtual position of each virtual segment and/or of each virtualarticulation deduced from the set of refined data is very close to thereal position of each real segment and/or each real articulation of theobserved person, thus permitting a precise analysis of the movements ofthe real segments and/or the real articulations of a person.

According to one embodiment of the invention, the processing module 32comprises at least an artificial neural network 42 having parametersoptimized by a learning method.

This artificial neural network 42 comprises a number of inputs oroutputs greater than or equal to the number of raw data to be processed.

According to one procedure, the number of raw data is forty-six (46).

According to one configuration, the artificial neural network 42comprises twenty-seven (27) inputs and twenty-seven (27) outputs.

According to one architecture, the artificial neural network 42comprises one hundred and eighteen (118) neurons, distributed in four(4) layers, twenty-seven (27) at each input or output layer andthirty-two (32) at each intermediate level. The artificial neuralnetwork 42 has a similar structure to a variational auto-encoder (VAE)structure.

In a variant, the artificial neural network 42 has a structure of therecurrent long short term memory (LSTM) type.

In a variant, the artificial neural network 42 is of the fully connectedtype, each neuron in a layer being connected to all the neurons in thelayer above.

The artificial neural network 42 usually comprises at least four layersof neurons.

According to one configuration, the artificial neural network 42comprises at least twenty-seven (27) inputs and twenty-seven (27)outputs.

According to a first configuration, the processing module 32 comprises asingle artificial neural network 42 configured for processing all theraw data. According to another configuration, the processing module 32comprises a plurality of artificial neural networks, each processingdifferent raw data. By way of example, a first network is configured forprocessing the raw data relating to the rotation vectors, and a secondnetwork is configured for processing the data relating to the lengths ofthe segments.

As illustrated in FIG. 1, the learning method comprises a step ofsimultaneous observations of a person 20 fitted with markers 34, by amovement analysis device 10 according to the invention and by areference analysis device 36.

The reference analysis device 36 comprises a plurality of cameras 38 anda processing system 40 configured for establishing a set of referencedata Y1 to Yn obtained as a result of the observation of the realsegments and/or the real articulations of the person 20. In a variant,the reference analysis device 36 comprises, in addition to the cameras38, at least one force sensor, for example a force plate.

This reference analysis device 36 is not described further, since it maybe identical to one of those of the first embodiment of the prior art.By way of example, the reference analysis device 36 may be a devicemarketed under the trade name Optitrack, comprising between 8 and 32cameras, or a device marketed under the trade name Vicon, comprising aforce plate.

As illustrated in FIG. 3, the raw data X1 to Xn obtained using themovement analysis device 10 are processed by the artificial neuralnetwork 42 of the processing module 32 so as to obtain a set of refineddata X1′ to Xn′. These refined data X1′ to Xn′ are compared, using aloss function 44, with the reference data Y1 to Yn obtained using thereference analysis device 36. On the basis of this comparison, theparameters of the artificial neural network 42 are modified so as tominimize the difference between the refined data X1′ to Xn′ and thereference data Y1 to Yn.

The modification of the parameters of the artificial neural network 42is based on the method of gradient back-propagation with the aim ofcorrecting the weights of the neurons, from the last layer to the first.

As the observations proceed, the processing module 32 becomesincreasingly precise, the refined data X1′ to Xn′ being substantiallyequal to the reference data Y1 to Yn obtained using the referenceanalysis device 36.

A number of movement analysis devices 10 may be calibratedsimultaneously. For each of them, the raw data X1 to Xn are processed bythe artificial neural network 42 of the processing module 32 so as toobtain a set of refined data X1′ to Xn′, which are compared with thereference data Y1 to Yn.

The movement analysis device 10 according to the invention enablesprecise monitoring of the segments of a person to be obtained. Since itscosts of manufacture and use are markedly lower than those of areference analysis device 36, the observations can be multiplied and thedevice according to the invention can be used in environments such asconstruction sites, for example.

The ability to multiply the observations makes it possible to collect alarge number of refined data relating to a multitude of movements ofdifferent persons, notably at different workstations.

Thus, it is possible to observe on several occasions the same movementperformed several times by different persons (of different builds).

The movement analysis device 10 comprises at least one database 46 whichrecords, for each movement, the different positions of the differentsegments at the different moments of the movement.

The observations of the same movement performed several times by severalpersons may be grouped in the database 46 in the form of a family ofmovements. For the purposes of the present application, a family ofmovements comprises similar movements, as in the case of the familyincluding movements corresponding to a stationary standing position, thefamily of movements concerned with picking up an object on the ground,the family of movements of torso rotation, the family of movements oflifting an arm, and so on.

Even if the movement analysis device 10 enables precise measurements tobe obtained, these measurements may be affected by noise due todifferent factors such as blocking phenomena or as a result of angles ofvision or the external environment. Thus, as illustrated in FIGS. 4 and5, movement analysis device 10 can determine, on the basis of a movementof real segments 22.1 to 22.3 comprising a plurality of successive realpositions P1 to P5 of the real segments, the virtual positions P1′, P3′,P4′, P5′ of the virtual segments 22.1′ to 22.3′ (corresponding to thereal segments 22.1 to 22.3). In this case, the virtual position of thevirtual segments corresponding to the position P2 could not bedetermined.

The movement analysis device 10 comprises a movement analysis moduleconfigured for identifying the observed movement.

According to a first embodiment, the movement analysis module isconfigured for comparing the observed movement with the movements storedin the database 46, and for identifying the family of movements to whichit belongs.

According to a first procedure, a method for analyzing a person'smovements comprises a step of segmenting a movement determined by theprocessing module 32 into a succession of elementary actions havingequal durations of between 0.25 and 1 s, and a step of translating eachelementary action into a character of a writing system.

For this purpose, the analysis device comprises an analysis module 48configured for segmenting the movement determined by the processingmodule 32 and translating each segment into a character of a writingsystem.

Each movement is translated into a succession of characters which,depending on the complexity and/or the length of the movement, forms asyllable, a word or a phrase.

According to another particular feature, each movement determined by theprocessing module 32 is stored, in the database 46 or another database46′, in the form of a succession of segments, each segment beingtranslated into a character of a writing system. Thus, on the basis of amultitude of observations of a multitude of movements translated in thedatabase 46, 46′ in the form of syllables, words or phrases, it ispossible to establish a structured written language that has at leastone rule, or usually a set of rules (such as grammatical rules, forexample) and at least one lexicon.

If it has been impossible to determine at least one elementary action ofa movement by the movement analysis device 10, this movement analysismethod makes it possible to determine the missing character on the basisof the rules and/or the lexicon of the structured written language, andby following the elementary action of which the determined character isthe translation.

As illustrated in FIGS. 4 and 6, on the basis of the movement of realsegments 22.1 to 22.3 visible in FIG. 4, the analysis module 48 segmentsthe observed movement into a succession of five elementary actionstranslated by “D”-“E”-“B”-“U”-“T”. Thus the movement of raising a handis translated by the word DEBUT in the structured written language.

As illustrated in FIGS. 5 and 6, if the processing module 32 identifiesonly the elementary actions 1, 3, 4 and 5 of the movement, asillustrated in FIG. 5, the analysis module 48 translates only theelementary actions 1, 3, 4 and 5 into a “D”, a “B”, a “U”, and a “T”according to structured written language. On the basis of the lexicon ofthe structured written language, the analysis module 48 deduces that themissing letter is probably an “E”, and that the observed movement,translated into DEBUT in the structured written language, corresponds tothe movement of raising a hand.

According to another distinctive feature, the fact that each movement istranslated into a string of characters may make it possible to comparethe character strings with each other in terms of strenuousness, and toassign a degree of strenuousness to each of the character strings on thebasis of their classification.

According to a second procedure illustrated in FIGS. 11 and 12, themethod for analyzing a person's movements comprises a first step ofsegmenting at least one sequence of movements into time units t0 to tn,n being an integer. Each time unit t0 to tn has a duration of between0.25 and 1 s. For the remainder of the description, each time unit t0 totn is associated with an instant t0 to tn. The sequence of movementscomprises at least one movement.

Thus, a sequence of movements Mvt of a person comprises a succession ofelementary actions, usually denoted Ace (W4, Y7, V5, . . . ), with oneelementary action for each time unit. Thus all the elementary actionshave the same duration of between 0.25 and 1 s.

By way of example, a sequence of movements of a walking person comprisesthe following elementary actions: “raise right leg”, “put down rightleg”, “raise left leg”, “put down left leg”, and so on.

The method for analyzing a person's movements comprises a step ofdetermining, for each instant t0 to tn, the position of at least somearticulations A, the position being determined by the acquisition unit12. According to one embodiment, the position of each articulation A0 toA13 is determined.

The position of the articulations A0 to A13 is given within at least onereference frame R, preferably linked to the person observed. Accordingto one configuration, a main reference frame R is positioned at thepelvis of the person observed.

According to a first variant, the position of each articulation A0 toA13 is given, at each instant, in spherical coordinates (three angles),on a step by step basis, assuming that the distance between twoarticulations linked by a single segment is constant. Thus, for a firstarticulation A7 or A0 linked to the main reference frame R, its positionis given in spherical coordinates in the main reference frame R,assuming that the distance A0A7 is constant. For a second articulationA8 (or A11), linked to the first articulation A7, its position is givenin spherical coordinates in a secondary reference frame linked to thefirst articulation A7, assuming that the distance A7A8 (or A7A11) isconstant. Similarly, for a second articulation A1 (or A4) linked to thefirst articulation A0, its position is given in spherical coordinates ina secondary reference frame linked to the first articulation A0,assuming that the distance A0A1 (or A0A4) is constant. For a thirdarticulation A2 (or A5, or A9, or A12), linked respectively to a secondarticulation A1 (or A4, or A8, or A11), its position is given inspherical coordinates in a third-level reference frame linked to thesecond articulation A1 (or A4, or A8, or A11). For a fourth articulationA3 (or A6, or A10, or A13), linked respectively to a third articulationA2 (or A5, or A9, or A12), its position is given in sphericalcoordinates in a fourth-level reference frame linked to the thirdarticulation A2 (or A5, or A9, or A12).

The position of each articulation A0 to A13 comprises at least oneangular value. If the articulation has a single degree of freedom, itsposition comprises a single angular value. If the articulation has twodegrees of freedom, its position comprises two angular values, one foreach degree of freedom. Finally, if the articulation has three degreesof freedom, its position comprises three angular values, one for eachdegree of freedom. Thus the position of each articulation comprises oneangular value for each degree of freedom of the articulation.

For each degree of freedom of each articulation, the angular valuevaries over an angular sector bounded by first and second limits. In oneembodiment, for each degree of freedom of a given articulation, thefirst and second limits are the articulation limits of the degree offreedom of the given articulation.

According to a distinctive feature of the invention, each angular sectorof each degree of freedom of each articulation is divided into aplurality of ranges of angular values of equal size. According to oneembodiment, each range of angular values extends over about 10 degreesat least. According to a preferred, but non-limiting, embodiment, eachrange of angular values extends over about 10 degrees.

As indicated above, the data relating to the positions of thearticulations determined by the acquisition unit 12 are raw, and arerefined by the processing module 32 and/or the analysis module 48.

The method for analyzing a person's movements comprises a step ofassigning at least one position code for at least some articulations A0to A13, preferably for each of the articulations A0 to A13, at eachinstant t0 to tn.

According to one configuration, each degree of freedom of eacharticulation is associated with at least one letter: F, I, P, RA, etc.Each range of angular values of each degree of freedom of eacharticulation is associated with a whole number, the numbers associatedwith the ranges of an angular sector extending from the first limit tothe second limit corresponding to the whole number of an arithmeticprogression with a common difference of 1.

The position code of an articulation at an instant t comprises at leastone pair consisting of at least one letter associated with one number,the letter corresponding to one of the degrees of freedom of thearticulation and the number corresponding to the range of angular valuesto which the angular value of the degree of freedom of the articulationbelongs at the instant t. For a given articulation, between twosuccessive instants, the position code comprises only the letter(s) andnumber pair associated with the degree of freedom whose angular valuehas varied between the two successive instants.

The method for analyzing a person's movements comprises a step ofdetermining at least one elementary action code at each instant t0 totn.

According to one configuration, the elementary action code at an instantt comprises at least one pair comprising at least a letter associatedwith a number.

Each of the combinations of position codes of the differentarticulations A0 to A13 at a given instant t is associated with anelementary action code at the given instant t.

Thus, by way of example, as illustrated in FIG. 11, a movement Mvtdenoted G7 comprises the succession of elementary actions W4, Y7, V5, W4corresponding to the combination of position codes F3, F5, 16, . . . ,R4.

The method for analyzing a person's movements comprises a step ofsyntactically verifying the elementary action codes in order to refinethe raw data relating to the positions of the articulations.

As for the first procedure, on the basis of a multitude of observationsof a multitude of movements translated in the form of syllablescorresponding to the position codes, words corresponding to theelementary action codes, or phrases corresponding to the movements, itis possible to establish a structured language that has at least onerule, or usually a set of rules (such as grammatical rules, for example)and at least one lexicon.

Because of this structured language, the step of syntactic verificationcomprises verifying whether the elementary action codes and/or theassigned position codes, deduced from the raw data acquired by theacquisition unit 12, comply with the grammatical rule(s) and thelexicon(s) of the structured languages, and correcting them ifnecessary.

As before, each rule and each lexicon of the structured language arestored in at least one database 46, 46′, and the steps of segmenting,determining the positions of the articulations, assigning positioncodes, assigning elementary action codes and syntactic verification areexecuted by the processing module 32 and/or the analysis module 48forming the artificial neural network 42.

To refine the syntactic verification, the method of analysis may bebased on a plurality of structured languages having rules and a lexicalfield common to all the persons observed and at least one rule and/or atleast one element of the lexical field that differ from one observedperson to another. Thus, each person observed may be associated with hisown structured language, different from the structured language ofanother person. In this case, the method of analysis comprises a step ofselecting the structured language associated with the person observedfor the step of syntactic analysis.

According to another variant illustrated in FIG. 12, the data relatingto the articulations at each given instant correspond to the distancesseparating each articulation from the other articulations. By way ofexample, as illustrated in FIG. 12, the data relating to thearticulations A0 to A6 at an instant th are identified. For the sake ofsimplicity, only the articulations of the upper limbs are identified.Evidently, the invention is not limited to the articulations of theupper limbs, and may be applied to all articulations. Thus, the distancebetween the articulations A3A5 is shown at the intersection of column A3and row A5, or at the intersection of column A5 and row A3.

Some distances, for example those between two articulations separated bya single segment, are fixed in time. Other distance, such as thosebetween two articulations separated by at least two segments, vary as afunction of time during a movement of the person observed.

Each distance between two articulations varies within a set of valuesbounded by the first and second limits, which are a function of themorphology of the person observed.

For each pair of articulations, the set of values is divided into aplurality of ranges of values of equal size. According to oneembodiment, each range of values extends over about 5 centimeters atleast.

As indicated above, the data relating to the distances between thedifferent pairs of articulations are determined by the acquisition unit12. These data are raw, and must be refined by the processing module 32and/or the analysis module 48.

According to a preferred embodiment, for each pair of articulations, ateach given instant, only the range of values to which the valuedetermined by the acquisition unit 12 belongs is identified. Each rangeof values is identified by an alphanumeric reference L1 to L15 and/or bya color. In the case of a color, the color code is chosen on the basisof the position of the range of values with respect to the first andsecond limits, the color code being, for example, increasingly darker asthe range of values approaches the first or second limit.

As for the angular values, the method of analysis comprises a step ofassigning, at each instant t0 to tn, at least one position code for thedifferent pairs of articulations, as a function of the range of valuesdetermined for each of the pairs, so as to form a combination ofposition codes at each instant t0 to tn; a step of assigning at leastone elementary action code for each given instant t0 to tn correspondingto the combination of position codes at the given instant t0 to tn; anda step of syntactically verifying the elementary action codes and/or theposition codes on the basis of at least one structured language.

Evidently, the invention is not limited to the articulations. Thus, themethod of analysis could be implemented on the basis of othercharacteristic points of the person observed by an acquisition system,such as the acquisition unit 12 for example, supplying raw data relatingto the positions of said characteristic points.

Furthermore, the position code is not limited to a letter and numberpair. More generally, the position code assigned at a given instantcomprises, for each degree of freedom of each articulation, at least onecharacter (such as a letter and number pair) corresponding to the rangeof angular values which includes the angular value determined at thegiven instant for the degree of freedom of the articulation.

Additionally, each position code and/or each elementary action code maybe associated with a strenuousness factor for the purpose of deducingtherefrom an overall strenuousness factor, based notably on therepetition of the movement Mvt and/or of the elementary action Ace.

In structural terms, each acquisition unit 12 may comprise, in additionto the sensor(s) 26, the processing module 32 and components 50 such asa battery, a cooling system to make the acquisition unit 12 independent,and a communication module 52 to enable the acquisition unit 12 tocommunicate with other elements such as a server, a remote computer, atablet or other elements, as illustrated in FIG. 8.

In one embodiment, the processing and analysis modules 32, 48 and thedatabase(s) 46, 46′ are integrated into a computer 54. This computer maycomprise, among other elements, a control module 56 for controlling eachsensor 26, a display module 58, and a communication module 60, to enablethe computer to communicate with a network 62 and/or with another device64 (computer, tablet or similar).

The movement analysis device 10 may comprise at least one attachedsensor 66, such as a temperature sensor, or an inertial unit. The signalfrom each attached sensor 66 may be transmitted to the processing module32.

At least one of the databases may be stored in a remote server 68.

Evidently, the invention is not limited to the embodiments describedabove.

While at least one exemplary embodiment of the present invention(s) isdisclosed herein, it should be understood that modifications,substitutions and alternatives may be apparent to one of ordinary skillin the art and can be made without departing from the scope of thisdisclosure. This disclosure is intended to cover any adaptations orvariations of the exemplary embodiment(s). In addition, in thisdisclosure, the terms “comprise” or “comprising” do not exclude otherelements or steps, the terms “a” or “one” do not exclude a pluralnumber, and the term “or” means either or both. Furthermore,characteristics or steps which have been described may also be used incombination with other characteristics or steps and in any order unlessthe disclosure or context suggests otherwise. This disclosure herebyincorporates by reference the complete disclosure of any patent orapplication from which it claims benefit or priority.

1-9. (canceled)
 10. A method for analyzing at least one sequence ofmovements performed by a person having a set of characteristic points,each movement comprising a succession of elementary actions, said methodcomprising: a step of observing the sequence of movements using at leastone acquisition system configured for supplying raw data relating topositions of at least some characteristic points of the set ofcharacteristic points, a step of segmenting the sequence of movementsinto time units, each associated with an instant, a step of determining,for each instant, the positions of at least some characteristic pointsof the set of characteristic points, using the acquisition system, astep of assigning, at each instant, at least one position code for atleast some characteristic points of the set of characteristic points, asa function, for each of them, of the determined position, to form acombination of position codes at each instant; a step of assigning atleast one elementary action code for each given instant corresponding tothe combination of position codes at the given instant; and a step ofsyntactically verifying at least one of the elementary action codes orthe position codes based on at least one structured language which hasat least one grammatical rule and at least one lexicon, in order torefine the raw data relating to the positions of the characteristicpoints.
 11. The method for analyzing as claimed in claim 10, wherein theelementary actions have equal durations of between 0.25 and 1 s.
 12. Themethod for analyzing as claimed in claim 10, wherein the position ofeach characteristic point is given in spherical coordinates in at leastone reference frame linked to the person.
 13. The method for analyzingas claimed in claim 10, wherein the characteristic points arearticulations of the person observed.
 14. The method for analyzing asclaimed in claim 13, wherein a position of each articulation comprisesan angular value for each degree of freedom of the articulation, varyingover an angular sector bounded by first and second limits.
 15. Themethod for analyzing as claimed in claim 14, wherein each angular sectoris divided into a plurality of ranges of angular values, of equal size,each range extending over about 10 degrees at least, and wherein theposition code assigned at a given instant comprises, for each degree offreedom of each articulation, at least one character corresponding tothe range of angular values which includes the angular value, determinedat the given instant, of the degree of freedom of the articulation. 16.The method for analyzing as claimed in claim 14, wherein, for eachdegree of freedom of each articulation, the first and second limits arearticulation limits of the degree of freedom of the articulation. 17.The method for analyzing as claimed in claim 10, wherein the step ofsyntactic verification comprises verifying whether or not at least oneof the elementary action codes or the assigned position codes complywith the at least one grammatical rule and the at least one lexicon ofthe structured languages, and correcting them if necessary.
 18. A devicefor analyzing at least one sequence of movements performed by a person,for the implementation of the method of analysis as claimed in claim 10,the person having a set of characteristic points, each movementcomprising a succession of elementary actions, said analysis devicecomprising at least an acquisition system configured for supplying rawdata relating to positions of at least some characteristic points of theset of characteristic points, wherein the analysis device comprises atleast one of a processing module or an analysis module including anartificial neural network configured for executing the steps ofsegmenting, determining the positions of the articulations, assigningposition codes, assigning elementary action codes, and syntacticverification, together with at least one database for storing eachgrammatical rule and each lexicon of the structured language.